CBMS Regional Conference – Bayesian Nonparametric Statistical Methods: Theory and Applications. Santa Cruz, California, August 16-20, 2010.
Bayesian nonparametric (BNP) methods combine the advantages of Bayesian modeling (e.g., ability to incorporate prior information, full and exact inference, ready extensions to hierarchical settings) with the appeal of nonparametric inference. In particular, they provide data-driven, albeit model-based, inference and, importantly, more reliable predictions than parametric models.
Theoretical research on NPB methods and their applications has grown dramatically in the last fifteen years. This has produced a massive body of scattered literature, which can be daunting for newcomers and hard to follow even for specialists. This CBMS conference, to be held between August 16th and August 20th, 2010 at the campus of the University of California, Santa Cruz, aims at providing a comprehensive introduction to the field for new researchers, and in particular graduate students, postdocs and junior researchers.
The main lecturer for the conference will be Dr. Peter Muller, who is Robert R. Herring Distinguished Professor in Clinical Research in the Department of Biostatistics at the M.D. Anderson Cancer Center, Houston, TX. In addition to the ten lectures delivered by Dr. Muller, four invited speakers will deliver complementary two-hour lectures. These invited speakers include Michael Jordan (University of California, Berkeley), Peter Hoff (University of Washington), Wesley Johnson (University of California, Irvine) and Tim Hanson (University of Minnesota). The local organizers are Abel Rodriguez and Athanasios Kottas.
More information can be found at the conference website https://www.ams.ucsc.edu/CBMS-NPBayes or through email at CBMS-NPB@ams.ucsc.edu